A "KU" new sensor for cytosolic DNA in T cells

Innate immune sensing of macromolecule homeostasis

The innate immune system uses a distinct set of germline-encoded pattern recognition receptors to recognize molecular patterns initially thought to be unique to microbial invaders, named pathogen-associated molecular patterns. The concept was later further developed to include similar molecular patterns originating from host cells during tissue damage, known as damage-associated molecular patterns. However, recent advances in the mechanism of monogenic inflammatory diseases have highlighted a much more expansive repertoire of cellular functions that are monitored by innate immunity. Here, we summarize several examples in which an innate immune response is triggered when homeostasis of macromolecule in the cell is disrupted in non-infectious or sterile settings. These ever-growing sensing mechanisms expand the repertoire of innate immune recognition, positioning it not only as a key player in host defense but also as a gatekeeper of cellular homeostasis. Therapeutics inspired by these advances to restore cellular homeostasis and correct the immune system could have far-reaching implications.

Application of New Sensor Technology and Few-Shot Learning in Education Based on IoT Era

In recent years, with the rapid development of emerging Internet of Things technology and short-range wireless communication technology, smart healthcare monitoring network technology has become a research hotspot. It provides convenience for people and enhances the development of people's own healthcare awareness. This paper aims to study how to make its application in the field of smart healthcare education more applicable through the use of related technologies in the Internet of Things era and few-shot learning. For this reason, this paper proposes to optimize and improve the new sensor technology and the algorithm of few-shot learning, and to adjust some parameters as a whole. At the same time, related experiments and analysis are designed for the improved algorithm to study and understand its performance. The experimental results in this paper show that the improved algorithm improves its application effect by 36.9% and is relatively more applicable than the unimproved algorithm.